
import pandas as pd
import plotly.express as px
import numpy as np


# Takes quite a bit of time for date-inference
# Optimization: Consider manual caching in a dict (top StOvflw answer)
data = pd.read_csv("PecanStreet_Project/dataport-export_gas_oct2015-mar2016.csv")


print("Number of unique houses: ", len(data["dataid"].unique()))

Number of unique houses:  157


# Calculating Time Difference in Seconds 
# Caclulating Difference between readings (in order)
# Assumption: localminute column is clean and error free
merged_df = pd.DataFrame()
for k,df in data.groupby(["dataid"]): 
    df.sort_values(by=["localminute"], inplace=True)
    df["val_diff"] = df["meter_value"].diff()
    df["time_diff"] = pd.to_datetime(df['localminute'], utc=True, infer_datetime_format=True, cache=True).diff().astype('timedelta64[s]')
    merged_df = merged_df.append(df)


# How many readings do I have per house?  
keys, counts = zip(*[(k, len(df)) for k, df in merged_df.groupby("dataid")])
px.scatter(x=keys, y=counts, log_y=True, color=counts, title="Number of Readings per House").show()


# looks like some houses don't have a lot of readings. 
# Let's extract the house_ids
MIN_NUM_READING_THRESHOLD = 50
less_readings = [(x, y) for x, y in list(zip(keys, counts)) if y < MIN_NUM_READING_THRESHOLD]
less_readings

[(2814, 37),
 (2946, 45),
 (4671, 21),
 (4874, 2),
 (5545, 33),
 (6101, 3),
 (7566, 32),
 (9620, 23)]


# Q: get rid of these houses in less_readings before next cells?


keys, means, stds, maxs, mins = zip(*[(k, df["val_diff"].mean(), df["val_diff"].std(), df["val_diff"].max(), df["val_diff"].min()) for k, df in merged_df.groupby("dataid")])


px.scatter(x=keys, y=means, log_y=True, title="Mean Meter Diff per House").show()


px.scatter(x=keys, y=maxs, log_y=True, title="Max Meter Diff per House").show()


px.scatter(x=keys, y=mins, title="Min Meter Diff per House").show()


# Interesting. Some houses haev negative diff. Malfunctioning meters? 
# How many bad readings and how many houses?
negative_diff_df = merged_df[merged_df['val_diff'] < 0]
print(len(negative_diff_df))                # how many readings
print(negative_diff_df['dataid'].nunique()) # how many houses
print(negative_diff_df['dataid'].unique())  # which houses

1306
43
[  35   77   94  483  484 1042 1086 1185 1507 1556 1718 1790 1801 2129
 2335 2449 3134 3527 3544 3893 4031 4193 4514 4998 5129 5131 5193 5403
 5810 5814 5892 6836 7017 7030 7117 7739 7794 7989 8156 8890 9134 9639
 9982]


# When do the spikes happen for each house? 
# Note that this diff < 0 signifies represents the RIGHT END of a spike, _NOT_ PEAK.
spike_dict = {} # dataid: number of spikes, first_spike_time, last_spike_time, diff_first_last_in_hours
for k, df in negative_diff_df.groupby('dataid'): 
    num_spikes = len(df)
    first_spike_end = df['localminute'].iloc[0]
    last_spike_end = df['localminute'].iloc[-1]
    spike_dict[k] = (num_spikes, first_spike_end, last_spike_end, (pd.to_datetime(last_spike_end, utc=True)-pd.to_datetime(first_spike_end, utc=True)))
spike_dict

{35: (1,
  '2015-10-20 09:00:12-05',
  '2015-10-20 09:00:12-05',
  Timedelta('0 days 00:00:00')),
 77: (1,
  '2015-10-20 20:20:55-05',
  '2015-10-20 20:20:55-05',
  Timedelta('0 days 00:00:00')),
 94: (6,
  '2015-11-01 01:11:10-06',
  '2016-01-18 11:26:52.211985-06',
  Timedelta('78 days 10:15:42.211985')),
 483: (1,
  '2015-10-21 00:18:54-05',
  '2015-10-21 00:18:54-05',
  Timedelta('0 days 00:00:00')),
 484: (9,
  '2015-11-01 01:18:29-06',
  '2016-03-05 01:57:57.625975-06',
  Timedelta('125 days 00:39:28.625975')),
 1042: (1,
  '2015-10-07 14:32:08-05',
  '2015-10-07 14:32:08-05',
  Timedelta('0 days 00:00:00')),
 1086: (1,
  '2015-11-01 01:32:25-06',
  '2015-11-01 01:32:25-06',
  Timedelta('0 days 00:00:00')),
 1185: (135,
  '2015-10-17 10:51:09-05',
  '2015-12-13 01:27:54-06',
  Timedelta('56 days 15:36:45')),
 1507: (2,
  '2015-11-01 01:39:26-06',
  '2015-11-01 01:57:27-06',
  Timedelta('0 days 00:18:01')),
 1556: (12,
  '2015-10-17 12:01:05-05',
  '2015-12-11 02:14:36-06',
  Timedelta('54 days 15:13:31')),
 1718: (4,
  '2015-10-20 16:27:02-05',
  '2015-10-20 20:16:07-05',
  Timedelta('0 days 03:49:05')),
 1790: (1,
  '2015-10-20 16:12:26-05',
  '2015-10-20 16:12:26-05',
  Timedelta('0 days 00:00:00')),
 1801: (1,
  '2015-10-20 07:52:02-05',
  '2015-10-20 07:52:02-05',
  Timedelta('0 days 00:00:00')),
 2129: (3,
  '2015-11-01 01:24:54-06',
  '2015-11-01 01:55:52-06',
  Timedelta('0 days 00:30:58')),
 2335: (5,
  '2015-10-20 18:23:32-05',
  '2015-12-11 23:52:27-06',
  Timedelta('52 days 06:28:55')),
 2449: (93,
  '2015-10-17 15:44:26-05',
  '2015-12-13 01:34:19-06',
  Timedelta('56 days 10:49:53')),
 3134: (18,
  '2015-10-20 13:51:35-05',
  '2015-12-12 17:10:28-06',
  Timedelta('53 days 04:18:53')),
 3527: (1,
  '2015-11-01 01:11:57-06',
  '2015-11-01 01:11:57-06',
  Timedelta('0 days 00:00:00')),
 3544: (18,
  '2015-12-07 19:49:52-06',
  '2015-12-12 18:48:35-06',
  Timedelta('4 days 22:58:43')),
 3893: (2,
  '2015-11-01 01:15:36-06',
  '2015-11-01 01:31:36-06',
  Timedelta('0 days 00:16:00')),
 4031: (16,
  '2015-10-08 07:06:58-05',
  '2016-03-28 14:22:29.36998-05',
  Timedelta('172 days 07:15:31.369980')),
 4193: (1,
  '2015-11-01 01:23:17-06',
  '2015-11-01 01:23:17-06',
  Timedelta('0 days 00:00:00')),
 4514: (141,
  '2015-10-17 09:53:59-05',
  '2016-03-30 22:00:32.143429-05',
  Timedelta('165 days 12:06:33.143429')),
 4998: (1,
  '2015-10-20 07:38:11-05',
  '2015-10-20 07:38:11-05',
  Timedelta('0 days 00:00:00')),
 5129: (76,
  '2015-10-17 18:39:09-05',
  '2015-12-13 02:02:14-06',
  Timedelta('56 days 08:23:05')),
 5131: (1,
  '2016-03-31 00:33:36.630053-05',
  '2016-03-31 00:33:36.630053-05',
  Timedelta('0 days 00:00:00')),
 5193: (4,
  '2015-11-01 01:40:25-06',
  '2015-11-01 07:48:35-06',
  Timedelta('0 days 06:08:10')),
 5403: (156,
  '2015-10-17 10:18:23-05',
  '2016-03-30 22:02:41.698151-05',
  Timedelta('165 days 11:44:18.698151')),
 5810: (10,
  '2015-10-31 00:04:45-05',
  '2016-02-22 17:42:13.232644-06',
  Timedelta('114 days 18:37:28.232644')),
 5814: (1,
  '2015-11-01 01:57:39-06',
  '2015-11-01 01:57:39-06',
  Timedelta('0 days 00:00:00')),
 5892: (1,
  '2015-11-01 01:22:43-06',
  '2015-11-01 01:22:43-06',
  Timedelta('0 days 00:00:00')),
 6836: (51,
  '2015-10-17 12:36:50-05',
  '2015-12-12 23:56:47-06',
  Timedelta('56 days 12:19:57')),
 7017: (1,
  '2015-10-20 12:05:00-05',
  '2015-10-20 12:05:00-05',
  Timedelta('0 days 00:00:00')),
 7030: (90,
  '2015-10-17 10:25:30-05',
  '2015-12-13 01:20:11-06',
  Timedelta('56 days 15:54:41')),
 7117: (123,
  '2015-10-17 00:07:17-05',
  '2016-03-30 23:06:13.750732-05',
  Timedelta('165 days 22:58:56.750732')),
 7739: (1,
  '2015-11-01 01:53:25-06',
  '2015-11-01 01:53:25-06',
  Timedelta('0 days 00:00:00')),
 7794: (1,
  '2015-11-01 08:01:58-06',
  '2015-11-01 08:01:58-06',
  Timedelta('0 days 00:00:00')),
 7989: (2,
  '2015-11-01 01:45:28-06',
  '2015-12-13 00:01:18-06',
  Timedelta('41 days 22:15:50')),
 8156: (151,
  '2015-10-17 13:06:23-05',
  '2015-12-13 00:53:41-06',
  Timedelta('56 days 12:47:18')),
 8890: (44,
  '2015-10-01 19:07:10-05',
  '2016-03-26 15:03:15.534834-05',
  Timedelta('176 days 19:56:05.534834')),
 9134: (115,
  '2015-10-17 13:27:19-05',
  '2015-12-13 02:01:12-06',
  Timedelta('56 days 13:33:53')),
 9639: (2,
  '2015-12-22 00:27:07.912441-06',
  '2015-12-22 11:54:52-06',
  Timedelta('0 days 11:27:44.087559')),
 9982: (2,
  '2015-12-08 20:31:41-06',
  '2015-12-11 12:29:49-06',
  Timedelta('2 days 15:58:08'))}


# Looks like it happens throughout the time period we have
# TODO: need to create a list for each house for when do the spikes happen? 
px.scatter(negative_diff_df, x='localminute')


keys, means, stds, maxs, mins = zip(*[(k, df["time_diff"].mean(), df["time_diff"].std(), df["time_diff"].max(), df["time_diff"].min()) for k, df in merged_df.groupby("dataid")])

px.scatter(x=keys, y=means, log_y=True, title="Mean Time between readings (s) per House").show()
px.scatter(x=keys, y=mins, title="Minimum Time between readings (s) per House").show()
px.scatter(x=keys, y=maxs, log_y=True, title="Max Time between readings (s) Per House").show()
px.scatter(x=keys, y=stds, log_y=True, title="Standard Deviation of Time between (s) readings by House").show()


# Resampling per hour

resampled_df = pd.DataFrame()

for k, df in merged_df.groupby("dataid"): 
    df = df.set_index(pd.DatetimeIndex(pd.to_datetime(df['localminute'], utc=True, infer_datetime_format=True, cache=True)))
    df.drop(columns=["localminute"], inplace=True)

    df.drop(columns=["val_diff", "time_diff"], inplace=True)

    # keep last sample of every hour only
    # missing hours become NA
    sample = df.resample('1h').last()

    # fix data_id for missing rows
    sample["dataid"].fillna(k, inplace=True)

    # TODO configurable hyperparameter for "fixing data"
    sample["meter_value"] = sample["meter_value"].interpolate()

    resampled_df = resampled_df.append(sample)


resampled_december_data = resampled_df.loc['2015-12-01':'2015-12-31']
px.line(resampled_december_data, x=resampled_december_data.index, y="meter_value", color="dataid").show()


# Question: How do we clkean the negative readings? 
#     Option 1: Modify merged_df so that diff = 0 
#     Option 2: Get rid of the rows in merged_df so our fill can path it automatically (OPTION CHOSEN IN CODE BELOW)

spikeless_resampled_df = pd.DataFrame()

for k, df in merged_df.groupby("dataid"): 
    df = df.set_index(pd.DatetimeIndex(pd.to_datetime(df['localminute'], utc=True, infer_datetime_format=True, cache=True)))
    df.drop(columns=["localminute"], inplace=True)

    spikeless_df = df[~(df['val_diff'].shift(-1) < 0)] 
    spikeless_df['val_diff'] = spikeless_df['meter_value'].diff()


    # Need to do this because some spikes are less "sharp" than 1 timestemp
    for i in range(10):  # by right should be doing UNTIL no more spikes left. Tested to see no more spikes after 10 passes
        spikeless_df = spikeless_df[~(spikeless_df['val_diff'].shift(-1) < 0)] 
        spikeless_df['val_diff'] = spikeless_df['meter_value'].diff()

    spikeless_df.drop(columns=["val_diff", "time_diff"], inplace=True)

    spikeless_sample = spikeless_df.resample('1h').last()

    spikeless_sample["dataid"].fillna(k, inplace=True)

    spikeless_sample["meter_value"] = spikeless_sample["meter_value"].interpolate()

    spikeless_resampled_df = spikeless_resampled_df.append(spikeless_sample)

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/julio/.local/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# let's see how spikeless looks now. 
spikeless_resampled_december_data = spikeless_resampled_df.loc['2015-12-01':'2015-12-31']
px.line(spikeless_resampled_december_data, x=spikeless_resampled_december_data.index, y="meter_value", color="dataid").show()


px.line(spikeless_resampled_df, x=spikeless_resampled_df.index, y="meter_value", color="dataid").show()


# Correlation Running WITH spikes
grouped_df = resampled_df.groupby("dataid")
top5_pair = {}
results = {}
for k1, df_1 in grouped_df: 
    correlations = []
    for k2, df_2 in grouped_df: 
        if k1==k2: 
            continue 
        # Does this align data? If not, gotta manually align first. 
        # What about misaligned data? (eg: meter A started from Oct 10th, while meter B started from Oct 1st. 
        # Same concern about the last reading)
        correlation = df_1["meter_value"].corr(df_2["meter_value"])  
        correlations.append((correlation, k2))
    correlations.sort(reverse=True)
    results[k1] = correlations[:5]


# for item in results.items(): 
#     print(item)


# Correlation run WITHOUT spikes

grouped_df = spikeless_resampled_df.groupby("dataid")

results = {}
for k1, df_1 in grouped_df: 
    correlations = []
    for k2, df_2 in grouped_df: 
        if k1==k2: 
            continue 
        # Does this align data? If not, gotta manually align first. 
        # What about misaligned data? (eg: meter A started from Oct 10th, while meter B started from Oct 1st. 
        # Same concern about the last reading)
        correlation = df_1["meter_value"].corr(df_2["meter_value"])  
        correlations.append((correlation, k2))
    correlations.sort(reverse=True)
    results[k1] = correlations[:5]


for item in results.items(): 
    print(item)

(35.0, [(0.9997526748882072, 2449.0), (0.9995527395465583, 6863.0), (0.9995423898608131, 5810.0), (0.99947566231535, 1697.0), (0.9993505999285084, 7989.0)])
(44.0, [(0.9996792843637767, 7566.0), (0.9996028242950981, 6863.0), (0.9995196468176009, 9620.0), (0.999503348638707, 4874.0), (0.9992154382085652, 9600.0)])
(77.0, [(0.9992420189690208, 9849.0), (0.9984352815378519, 3036.0), (0.9982884405421145, 7566.0), (0.9981047819245014, 4874.0), (0.9980955191912054, 9600.0)])
(94.0, [(0.9996651618817822, 3778.0), (0.999621634813293, 9134.0), (0.999615729192582, 2335.0), (0.9995917014580785, 5892.0), (0.9995496551678821, 2575.0)])
(114.0, [(0.9998846813465284, 1556.0), (0.9998185636984711, 2094.0), (0.9998082966484926, 4514.0), (0.9997965461544297, 4228.0), (0.9997933892918263, 4031.0)])
(187.0, [(0.9997468147251951, 9766.0), (0.9996867348911814, 4296.0), (0.9996833231767409, 7741.0), (0.9996805136458671, 3036.0), (0.9996474851034776, 5892.0)])
(222.0, [(0.9913656165085389, 77.0), (0.990540140802083, 1283.0), (0.9894720249568137, 9849.0), (0.9869541504123012, 1697.0), (0.9852627186876579, 484.0)])
(252.0, [(0.9998272411675654, 4732.0), (0.9998146776789202, 4514.0), (0.9998063632970686, 661.0), (0.9997961896851346, 5814.0), (0.9997943790805235, 3893.0)])
(370.0, [(0.9996766483935462, 7117.0), (0.999523265085189, 6505.0), (0.9994543456228758, 6673.0), (0.9993629931363326, 744.0), (0.999345519315453, 6836.0)])
(483.0, [(0.9999050385652904, 3367.0), (0.999863153513334, 4193.0), (0.9998015280086298, 5439.0), (0.9996906639858844, 8829.0), (0.9996848754164985, 6412.0)])
(484.0, [(0.999785416895361, 5484.0), (0.9993040546728448, 1697.0), (0.9992199650448467, 187.0), (0.9991946330992387, 2575.0), (0.9991839800304779, 2449.0)])
(661.0, [(0.9999059950461868, 7794.0), (0.9998960492691288, 5814.0), (0.9998868706524996, 3723.0), (0.9998427484476502, 4514.0), (0.9998391692085358, 3893.0)])
(739.0, [(0.9998273976364757, 5814.0), (0.9998143732495809, 661.0), (0.9997538185693925, 7794.0), (0.9997277933282992, 9295.0), (0.9997269581214313, 3723.0)])
(744.0, [(0.9996839203218126, 7900.0), (0.9996306266884215, 1415.0), (0.9996019642784408, 6505.0), (0.999574853404382, 7989.0), (0.9995616576110591, 9474.0)])
(871.0, [(0.9996576351842827, 8890.0), (0.9996405767082968, 3577.0), (0.9996001142393539, 3918.0), (0.9995894506810372, 2072.0), (0.9995652932876423, 4193.0)])
(1042.0, [(0.9995635331353913, 9982.0), (0.9995287106452437, 7030.0), (0.9993984501260964, 3039.0), (0.9993609450332958, 8703.0), (0.9993232895035317, 5403.0)])
(1086.0, [(0.9998423208267735, 5972.0), (0.9998136071918919, 4356.0), (0.9997036468251512, 9121.0), (0.9996908673320393, 5395.0), (0.9996699026975234, 9639.0)])
(1103.0, [(0.9997766956540063, 3036.0), (0.9993936072314076, 2755.0), (0.9993119205082341, 2946.0), (0.9993099445198992, 2980.0), (0.9992432662129506, 6830.0)])
(1185.0, [(0.9996091727881671, 3036.0), (0.9994598257567798, 2755.0), (0.999367284560907, 3918.0), (0.9993029218298783, 5972.0), (0.9992430745434093, 2946.0)])
(1283.0, [(0.999083856321967, 3036.0), (0.9990655218089899, 9600.0), (0.9987305585297322, 9160.0), (0.9986064793412381, 7566.0), (0.998597052127993, 2946.0)])
(1403.0, [(0.9897451174163536, 739.0), (0.9897047690029551, 2945.0), (0.9896177672982167, 484.0), (0.9889250009644517, 4029.0), (0.9881155960461707, 35.0)])
(1415.0, [(0.9996306266884215, 744.0), (0.9995827247865519, 8086.0), (0.9994332711035365, 7741.0), (0.9994240843783777, 3036.0), (0.9994224938833213, 1801.0)])
(1507.0, [(0.9999251830434209, 2094.0), (0.9999161401330219, 5636.0), (0.9999056418335737, 6910.0), (0.9998705440095534, 4373.0), (0.9998538319179116, 7741.0)])
(1556.0, [(0.9999226572562063, 1619.0), (0.9998846813465284, 114.0), (0.9998741647708538, 3577.0), (0.9998740306791006, 4031.0), (0.9998666824396323, 4373.0)])
(1589.0, [(0.9998737607920329, 3527.0), (0.9997365007778254, 1714.0), (0.9997341319939893, 7117.0), (0.9996796203873528, 7900.0), (0.999669823057604, 5439.0)])
(1619.0, [(0.9999226572562063, 1556.0), (0.9998818635983886, 4031.0), (0.999832435911814, 3577.0), (0.9998145859907974, 2072.0), (0.9997908509756056, 2034.0)])
(1697.0, [(0.9995688764133107, 2449.0), (0.99947566231535, 35.0), (0.9994731838144182, 7965.0), (0.9993531869624956, 9620.0), (0.9993334524109129, 6863.0)])
(1714.0, [(0.9997365007778255, 1589.0), (0.9997267715775855, 3527.0), (0.9997055234912743, 5439.0), (0.9995409511401232, 3367.0), (0.9994634809965175, 9631.0)])
(1718.0, [(0.9998737050647694, 1791.0), (0.999870085087337, 1801.0), (0.9998226395122428, 2094.0), (0.9997915446615052, 9766.0), (0.9997767662507091, 4514.0)])
(1790.0, [(0.9994084759191343, 3723.0), (0.9993560843525757, 3134.0), (0.9993254578536268, 739.0), (0.9993098899355912, 9639.0), (0.99930846446137, 5814.0)])
(1791.0, [(0.9998737050647694, 1718.0), (0.9997611779592572, 1801.0), (0.9996960990821618, 2094.0), (0.9996421361173113, 9766.0), (0.9996421128726849, 7674.0)])
(1792.0, [(0.9996826528875996, 114.0), (0.9996249771958784, 1556.0), (0.999595075880649, 1619.0), (0.9995591271079667, 3893.0), (0.9995563597320494, 4514.0)])
(1800.0, [(0.9988827181359584, 5193.0), (0.9987709916425517, 1042.0), (0.9984742113157573, 3849.0), (0.9983342025175485, 7030.0), (0.9982466553145578, 8086.0)])
(1801.0, [(0.9998700850873371, 1718.0), (0.9998497338468649, 2094.0), (0.9998484314803383, 8086.0), (0.9998478967349876, 1507.0), (0.9998461601590549, 7741.0)])
(2018.0, [(0.9998721318770506, 8086.0), (0.9997933609766129, 9766.0), (0.9997761520916278, 1801.0), (0.9997661170194856, 7674.0), (0.9997154476674336, 9729.0)])
(2034.0, [(0.9998514147498442, 8829.0), (0.9998464326669135, 4031.0), (0.999814129933054, 3544.0), (0.9998017585353597, 9729.0), (0.9997908509756057, 1619.0)])
(2072.0, [(0.999871067857473, 4031.0), (0.9998375779906085, 3577.0), (0.9998145859907974, 1619.0), (0.9997777279556803, 2034.0), (0.999767747480838, 1556.0)])
(2094.0, [(0.9999251830434209, 1507.0), (0.9998860726950676, 6910.0), (0.9998858532433059, 5636.0), (0.9998758143737038, 4373.0), (0.9998694531402199, 4514.0)])
(2129.0, [(0.9995977642247346, 9134.0), (0.9995685640730392, 9121.0), (0.9995560657123165, 661.0), (0.9995503671863406, 4228.0), (0.9995361717402383, 3778.0)])
(2233.0, [(0.9997885942575445, 4421.0), (0.999625341020188, 2638.0), (0.9996090204555458, 739.0), (0.9995142744689915, 5814.0), (0.9994952001628815, 661.0)])
(2335.0, [(0.9997981918795754, 9134.0), (0.9997431852417366, 4373.0), (0.9997331717475386, 5636.0), (0.9997236883329917, 9052.0), (0.9997155346079901, 6910.0)])
(2378.0, [(0.999665316606243, 3036.0), (0.9993059097255554, 2755.0), (0.9993052994294587, 9600.0), (0.9992273499938297, 7566.0), (0.9991550339359326, 2946.0)])
(2449.0, [(0.9997953463952343, 5810.0), (0.9997526748882071, 35.0), (0.9996654719397229, 7989.0), (0.9995688764133106, 1697.0), (0.9995231217553201, 3036.0)])
(2461.0, [(0.9995390225831635, 483.0), (0.9995110538605744, 4193.0), (0.9994592097606178, 4767.0), (0.999428303992788, 7016.0), (0.9993719486935543, 5439.0)])
(2470.0, [(0.9997827540695632, 3527.0), (0.99974658479247, 7017.0), (0.9996950981474823, 8086.0), (0.9996335663444579, 3367.0), (0.9995977411759921, 3849.0)])
(2575.0, [(0.999735209127344, 7989.0), (0.999549655167882, 94.0), (0.9995284585454478, 7460.0), (0.9995227760782858, 5484.0), (0.999511307312328, 3778.0)])
(2638.0, [(0.9998929698996833, 4352.0), (0.99963620515607, 5131.0), (0.9996253410201877, 2233.0), (0.9995883344526104, 4732.0), (0.9995815709875123, 4421.0)])
(2645.0, [(0.9962723497759343, 5317.0), (0.996271457294071, 6673.0), (0.9962062258613821, 3036.0), (0.9954884788406098, 9600.0), (0.9954282037478722, 7566.0)])
(2755.0, [(0.9994977886306912, 3723.0), (0.9994598257567799, 1185.0), (0.9994373062331458, 3036.0), (0.999432721628693, 187.0), (0.9994204700604615, 2094.0)])
(2814.0, [(1.0, 6578.0), (0.9994503818940548, 5403.0), (0.9993753033109051, 2818.0), (0.9992854691177864, 1801.0), (0.999170616751205, 7287.0)])
(2818.0, [(0.9998667367030014, 9052.0), (0.9998344560932975, 4514.0), (0.9998257001190732, 2094.0), (0.9998210712391689, 3893.0), (0.9998097047458833, 4228.0)])
(2945.0, [(0.9995222170954426, 1791.0), (0.999485367377566, 2818.0), (0.9994853201708894, 2094.0), (0.999467348517943, 1718.0), (0.9994621708369856, 3544.0)])
(2946.0, [(0.999605288587587, 4373.0), (0.9995718906203337, 3036.0), (0.9995676767255529, 3723.0), (0.9995136382523176, 2449.0), (0.9995082018102153, 2965.0)])
(2965.0, [(0.9995082018102153, 2946.0), (0.9994102975930391, 3036.0), (0.9992901396009267, 5193.0), (0.9992707717380512, 7566.0), (0.9991017886953122, 2755.0)])
(2980.0, [(0.9995945000865102, 9631.0), (0.9994263684999112, 5545.0), (0.9993099445198992, 1103.0), (0.9993097907197819, 6836.0), (0.9992929712231395, 3527.0)])
(3036.0, [(0.9999039634543735, 4514.0), (0.9998570947714109, 1556.0), (0.9998382712190476, 3577.0), (nan, 4874.0), (0.9997780650298987, 6830.0)])
(3039.0, [(0.999659765567506, 5403.0), (0.9995515915922882, 9982.0), (0.9994088298189366, 187.0), (0.9993984501260963, 1042.0), (0.9993967909129465, 7989.0)])
(3134.0, [(0.9997313617369455, 3036.0), (0.9995931292842337, 9639.0), (0.9994632151279255, 1086.0), (0.9993560843525757, 1790.0), (0.9992517670081575, 9600.0)])
(3310.0, [(0.9996768164298285, 3036.0), (0.9996468118580285, 5545.0), (0.9994461728639575, 9631.0), (0.9994194542322412, 2755.0), (0.9993604452401508, 2946.0)])
(3367.0, [(0.9999050385652904, 483.0), (0.9998743313963907, 5439.0), (0.9998514151694978, 7017.0), (0.9997625097031435, 8829.0), (0.9997564258770544, 6412.0)])
(3527.0, [(0.9998737607920328, 1589.0), (0.999785487614017, 5439.0), (0.9997827540695633, 2470.0), (0.9997491320783535, 7017.0), (0.9997470020679398, 7900.0)])
(3544.0, [(0.9999608645731811, 8829.0), (0.9998473782970955, 9729.0), (0.9998301981197197, 7429.0), (0.9998200812887866, 4031.0), (0.999814129933054, 2034.0)])
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(9766.0, [(0.9998197630659923, 1801.0), (0.9997933609766128, 2018.0), (0.9997915446615052, 1718.0), (0.9997825310552213, 1507.0), (0.9997695235562689, 8086.0)])
(9849.0, [(0.9992420189690208, 77.0), (0.998535878172832, 9620.0), (0.9984787807665093, 7965.0), (0.9984289747520368, 5658.0), (0.9979127889867948, 1697.0)])
(9956.0, [(0.9994493865039913, 3544.0), (0.9994406853488469, 7566.0), (0.9994382302843654, 6412.0), (0.9994301745883041, 2034.0), (0.9994191468768163, 2470.0)])
(9982.0, [(0.9997072160077026, 5403.0), (0.9995635331353911, 1042.0), (0.9995515915922882, 3039.0), (0.9995095893923107, 6673.0), (0.9994682404356233, 3036.0)])


# Correlation run WITHOUT spikes and with align

grouped_df = spikeless_resampled_df.groupby("dataid")

results = {}
for k1, df_1 in grouped_df: 
    correlations = []
    for k2, df_2 in grouped_df: 
        if k1==k2: 
            continue 
        # Does this align data? If not, gotta manually align first. 
        # What about misaligned data? (eg: meter A started from Oct 10th, while meter B started from Oct 1st. 
        # Same concern about the last reading)
        df_1, df_2 = df_1.align(df_2, join='inner', axis=0) #Inner join to align data before calculating corr
        correlation = df_1["meter_value"].corr(df_2["meter_value"], method='pearson')  
        correlations.append((correlation, k2))
    correlations.sort(reverse=True)
    results[k1] = correlations[:5]


## default, no method specified
results

{35.0: [(0.9988916513276471, 1801.0),
  (0.9987725928850879, 484.0),
  (0.9987715294996004, 44.0),
  (0.998713916885043, 2378.0),
  (0.9982606893127842, 1086.0)],
 44.0: [(0.9989856933664459, 1697.0),
  (0.9989041421451049, 2378.0),
  (0.9988639130515852, 2449.0),
  (0.9987868775083375, 1507.0),
  (0.9987715294996004, 35.0)],
 77.0: [(0.9985464337783981, 2575.0),
  (0.9985076194482484, 2378.0),
  (0.9984191125503111, 1415.0),
  (0.9984157534979128, 1697.0),
  (0.9981987354565149, 1556.0)],
 94.0: [(0.9998004755147432, 2378.0),
  (0.9995318807806067, 1507.0),
  (0.9995211138004041, 2818.0),
  (0.9994101885024019, 739.0),
  (0.999398403326994, 1556.0)],
 114.0: [(0.9995625901432316, 1556.0),
  (0.9995259032246329, 252.0),
  (0.9995097537170333, 2094.0),
  (0.9995052281479414, 2034.0),
  (0.9994781195740005, 661.0)],
 187.0: [(0.9996430082465669, 1556.0),
  (0.9996247933971276, 2094.0),
  (0.9996236239207223, 2449.0),
  (0.999458838161354, 2755.0),
  (0.9993632834392135, 3723.0)],
 222.0: [(0.9933255909686177, 1792.0),
  (0.987455756777489, 77.0),
  (0.9867692728108842, 4373.0),
  (0.9867324504345368, 2461.0),
  (0.9862840412618783, 2018.0)],
 252.0: [(0.9997279929257872, 661.0),
  (0.9995259032246328, 114.0),
  (0.9995157424177141, 871.0),
  (0.9994350478915248, 739.0),
  (0.9991971819206549, 1086.0)],
 370.0: [(0.999127599958165, 744.0),
  (0.998664019717181, 483.0),
  (0.9980875242702468, 1103.0),
  (0.9973088725507759, 187.0),
  (0.9968760354735687, 871.0)],
 483.0: [(0.9992932447917133, 871.0),
  (0.9992379166376183, 744.0),
  (0.9992185336541498, 2449.0),
  (0.9991551464788697, 3527.0),
  (0.9991051184020254, 3544.0)],
 484.0: [(0.9992589707173078, 187.0),
  (0.9991456302178563, 94.0),
  (0.9989029010392995, 2645.0),
  (0.9985390350910787, 739.0),
  (0.9985181593655986, 35.0)],
 661.0: [(0.9998203224439808, 739.0),
  (0.9997279929257871, 252.0),
  (0.9996518015821799, 1086.0),
  (0.9996175273594168, 2470.0),
  (0.999550034823958, 1556.0)],
 739.0: [(0.9998203224439808, 661.0),
  (0.9995255976473749, 1086.0),
  (0.9994350478915247, 252.0),
  (0.9994101885024019, 94.0),
  (0.9991936747863985, 114.0)],
 744.0: [(0.9995735667074869, 2470.0),
  (0.9995002697668744, 1507.0),
  (0.9994565665602485, 1589.0),
  (0.9994206463073095, 1415.0),
  (0.9994123180849728, 2378.0)],
 871.0: [(0.9997048509428996, 2094.0),
  (0.9995548511870144, 1589.0),
  (0.9995414665914715, 1556.0),
  (0.9995382936172148, 1507.0),
  (0.9995300073983386, 3918.0)],
 1042.0: [(0.9994198055254866, 2449.0),
  (0.9991771763883043, 1556.0),
  (0.9991591340079327, 2094.0),
  (0.9990817031445499, 1791.0),
  (0.9990456887669522, 2018.0)],
 1086.0: [(0.999685551757991, 2470.0),
  (0.9996518015821799, 661.0),
  (0.9995971384695653, 1589.0),
  (0.999525597647375, 739.0),
  (0.9995187193372295, 4514.0)],
 1103.0: [(0.999683887579302, 1556.0),
  (0.9996678262957799, 2378.0),
  (0.9996577775127029, 2094.0),
  (0.9996324801206575, 4514.0),
  (0.9996230804359173, 1507.0)],
 1185.0: [(0.9997034596783285, 2094.0),
  (0.9996768908642149, 2072.0),
  (0.9996426598966431, 1556.0),
  (0.9995965426872312, 4514.0),
  (0.9995931851708231, 3577.0)],
 1283.0: [(0.9992176352938706, 1714.0),
  (0.9991278172941822, 2335.0),
  (0.9990089524765935, 1589.0),
  (0.9990029907935564, 2378.0),
  (0.9989542411305605, 1415.0)],
 1403.0: [(0.9897451174163536, 739.0),
  (0.9896177672982167, 484.0),
  (0.9881155960461707, 35.0),
  (0.9879151340445743, 2645.0),
  (0.9868592308125006, 1801.0)],
 1415.0: [(0.9996743645363465, 744.0),
  (0.9995934039919999, 2378.0),
  (0.9995863314292642, 2470.0),
  (0.9994820951716673, 1589.0),
  (0.99942433168233, 3036.0)],
 1507.0: [(0.9997372055250525, 2378.0),
  (0.999649232088274, 2470.0),
  (0.9995992381138943, 187.0),
  (0.9995927204572331, 4514.0),
  (0.9995873021708271, 114.0)],
 1556.0: [(0.9998933498113914, 114.0),
  (0.9997694273798102, 2094.0),
  (0.9997632943597805, 252.0),
  (0.9996783280223769, 4514.0),
  (0.9996781035702021, 3036.0)],
 1589.0: [(0.9998085434284717, 2470.0),
  (0.9997563466592754, 370.0),
  (0.9995665856418121, 2378.0),
  (0.9995493959613906, 1507.0),
  (0.9995372578435562, 3577.0)],
 1619.0: [(0.9998298806505843, 114.0),
  (0.9996773263599792, 252.0),
  (0.999534564008339, 871.0),
  (0.9993603325780044, 3527.0),
  (0.9993521536286348, 661.0)],
 1697.0: [(0.99947566231535, 35.0),
  (0.999311619841244, 484.0),
  (0.998968459694981, 44.0),
  (0.9986870233295796, 2449.0),
  (0.9985196708543893, 2755.0)],
 1714.0: [(0.9994142434800387, 370.0),
  (0.9991918643220102, 483.0),
  (0.9990445328581031, 1589.0),
  (0.9990062501079624, 744.0),
  (0.9989745625388377, 1791.0)],
 1718.0: [(0.9997872865364674, 114.0),
  (0.9992541984796071, 252.0),
  (0.9991615231282971, 187.0),
  (0.9991014495277059, 661.0),
  (0.9989328200121562, 744.0)],
 1790.0: [(0.9993789729715589, 661.0),
  (0.9993623832997084, 1086.0),
  (0.9992989659377743, 739.0),
  (0.9991014965417624, 252.0),
  (0.9990084896029384, 94.0)],
 1791.0: [(0.99962107205368, 114.0),
  (0.9993221460660745, 2072.0),
  (0.999249669851521, 2449.0),
  (0.9991528582701252, 2094.0),
  (0.9991334627067868, 744.0)],
 1792.0: [(0.9996099281809666, 114.0),
  (0.9990567332754001, 252.0),
  (0.9988921093584354, 661.0),
  (0.9987528761370718, 871.0),
  (0.9986080940758999, 483.0)],
 1800.0: [(0.9981463590886989, 1042.0),
  (0.9979106304415494, 1790.0),
  (0.9977219825647319, 2335.0),
  (0.99760046552228, 2018.0),
  (0.9971052267395082, 1714.0)],
 1801.0: [(0.9996588734806993, 114.0),
  (0.999466710997838, 187.0),
  (0.9993088569525405, 744.0),
  (0.9992062827275311, 252.0),
  (0.9991726411904781, 3778.0)],
 2018.0: [(0.9995986830953897, 187.0),
  (0.9990672899985119, 744.0),
  (0.9990359908051228, 114.0),
  (0.9988989995054944, 483.0),
  (0.9988575011475308, 252.0)],
 2034.0: [(0.9996444422373115, 114.0),
  (0.9995761571903782, 2094.0),
  (0.999569178956808, 483.0),
  (0.9995426372626885, 1556.0),
  (0.9994494015696319, 744.0)],
 2072.0: [(0.9996191304220696, 871.0),
  (0.9995942973866445, 114.0),
  (0.9995262586060853, 1556.0),
  (0.9995129941283548, 2094.0),
  (0.9994418293561942, 483.0)],
 2094.0: [(0.9998384142965656, 114.0),
  (0.9997694273798101, 1556.0),
  (0.9996558553649325, 3577.0),
  (0.9996270711430336, 4514.0),
  (0.999586880872814, 3036.0)],
 2129.0: [(0.9994983744021524, 661.0),
  (0.9993357860199464, 739.0),
  (0.9992152607875646, 252.0),
  (0.9990927281920403, 1086.0),
  (0.9990056628948879, 94.0)],
 2233.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4421.0),
  (1.0, 4352.0),
  (1.0, 3039.0)],
 2335.0: [(0.9995752664364121, 252.0),
  (0.9994894944063974, 187.0),
  (0.9994708393059075, 94.0),
  (0.9994170499614686, 661.0),
  (0.9993598713819227, 114.0)],
 2378.0: [(0.9997372055250525, 1507.0),
  (0.9996104973147809, 3036.0),
  (0.999593403992, 1415.0),
  (0.9995856430005383, 2470.0),
  (0.9995665856418121, 1589.0)],
 2449.0: [(0.9997526748882071, 35.0),
  (0.9995777161954484, 4514.0),
  (0.9995068292476748, 3577.0),
  (0.9994704370582153, 3036.0),
  (0.9994430120994543, 2755.0)],
 2461.0: [(0.9994693096312934, 483.0),
  (0.9991083046270173, 3527.0),
  (0.9991022655165921, 4228.0),
  (0.9990572957986681, 3918.0),
  (0.9989596425556287, 3544.0)],
 2470.0: [(0.9998085434284717, 1589.0),
  (0.9996492320882742, 1507.0),
  (0.9996352527056601, 4514.0),
  (0.9995863314292642, 1415.0),
  (0.9995856430005383, 2378.0)],
 2575.0: [(0.9995860815920268, 484.0),
  (0.9995077005270985, 94.0),
  (0.9994453288516358, 187.0),
  (0.999361579724732, 2378.0),
  (0.9992865726844591, 1415.0)],
 2638.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4421.0),
  (1.0, 3039.0),
  (1.0, 2233.0)],
 2645.0: [(0.9985257731900775, 3778.0),
  (0.9981772506153702, 2233.0),
  (0.9981772506153695, 2638.0),
  (0.9979955837800468, 4356.0),
  (0.9979063227732246, 4296.0)],
 2755.0: [(0.9994598257567799, 1185.0),
  (0.9994526583617951, 3036.0),
  (0.9994499052795099, 2818.0),
  (0.9994430120994543, 2449.0),
  (0.999432721628693, 187.0)],
 2814.0: [(0.9996589952200725, 4447.0),
  (0.999658995220047, 4671.0),
  (0.9996589952200465, 4352.0),
  (0.9996589952200464, 3039.0),
  (0.9996589952200463, 4421.0)],
 2818.0: [(0.9997024407739498, 114.0),
  (0.9995675796292892, 2378.0),
  (0.9995627582515842, 661.0),
  (0.9995428357385097, 871.0),
  (0.999536176788721, 252.0)],
 2945.0: [(0.999447495010992, 114.0),
  (0.9990494990212689, 744.0),
  (0.9987924534721448, 661.0),
  (0.9987009724200586, 483.0),
  (0.9984915216713997, 252.0)],
 2946.0: [(0.9996055834067953, 3527.0),
  (0.999538739231029, 3036.0),
  (0.9995382569334806, 4514.0),
  (0.9995136061151717, 2449.0),
  (0.999429876576295, 187.0)],
 2965.0: [(0.9994910624285592, 2449.0),
  (0.9992476082810565, 3036.0),
  (0.9991448162718293, 1507.0),
  (0.9991108031702451, 1556.0),
  (0.9991101911760899, 2755.0)],
 2980.0: [(0.9993099445198992, 1103.0),
  (0.9988749400669686, 3544.0),
  (0.9988127938929651, 2470.0),
  (0.9987993205584839, 1589.0),
  (0.9987847040963413, 370.0)],
 3036.0: [(0.9998555225216667, 1556.0),
  (0.9998157838802102, 4514.0),
  (0.9997766956540064, 1103.0),
  (0.9997243510866646, 3577.0),
  (0.9997116271624364, 2094.0)],
 3039.0: [(1.0, 4671.0),
  (1.0, 4421.0),
  (0.9999999999999999, 2233.0),
  (0.9999999999999998, 4447.0),
  (0.9999999999999997, 4352.0)],
 3134.0: [(0.9997447861911367, 4514.0),
  (0.9997308335840233, 3577.0),
  (0.9997302777978476, 1507.0),
  (0.9996601198708045, 2378.0),
  (0.9996493246628915, 1556.0)],
 3310.0: [(0.9995949170315747, 2449.0),
  (0.9995800574511927, 4514.0),
  (0.9995661366040292, 1556.0),
  (0.9995350369438913, 3577.0),
  (0.9995146734268959, 2094.0)],
 3367.0: [(0.9998824945042276, 483.0),
  (0.9993913513401642, 3544.0),
  (0.9993442979976594, 744.0),
  (0.999315152045406, 3918.0),
  (0.9992848213413831, 3527.0)],
 3527.0: [(0.9997741006530172, 2470.0),
  (0.9997268040643851, 1589.0),
  (0.9997136682853142, 1507.0),
  (0.9996846143535957, 2378.0),
  (0.9996681610950429, 3918.0)],
 3544.0: [(0.9996413403286801, 744.0),
  (0.9995871561819358, 114.0),
  (0.9995735411958593, 2449.0),
  (0.9995710007944887, 483.0),
  (0.9993913513401642, 3367.0)],
 3577.0: [(0.9998577110523537, 4514.0),
  (0.9998138925871303, 1556.0),
  (0.9998123575190337, 252.0),
  (0.999780662843521, 114.0),
  (0.999763356310254, 2094.0)],
 3635.0: [(0.9992305101708476, 661.0),
  (0.9988649665270694, 739.0),
  (0.9988211126422571, 252.0),
  (0.9987262025664542, 1086.0),
  (0.9985737333797565, 114.0)],
 3723.0: [(0.9999182023209029, 661.0),
  (0.9996872026561977, 739.0),
  (0.9996651511398912, 1086.0),
  (0.9996336507144408, 2449.0),
  (0.9996117062026834, 252.0)],
 3778.0: [(0.9997195437794463, 661.0),
  (0.9996174959019922, 739.0),
  (0.9996007610285859, 252.0),
  (0.9995426442694233, 114.0),
  (0.9995102305345287, 94.0)],
 3849.0: [(0.9990396427652662, 483.0),
  (0.9989124327033737, 2755.0),
  (0.9987374649154317, 2094.0),
  (0.9987038334374873, 1556.0),
  (0.9986925866443158, 2449.0)],
 3893.0: [(0.9998039461288494, 661.0),
  (0.999796513511127, 252.0),
  (0.9997634072930665, 114.0),
  (0.9995867396094569, 739.0),
  (0.9993060956726536, 871.0)],
 3918.0: [(0.9997202681158402, 252.0),
  (0.999668161095043, 3527.0),
  (0.9996142098977864, 1086.0),
  (0.9995629971808726, 661.0),
  (0.9994985556551076, 2449.0)],
 4029.0: [(0.9977326771902556, 1042.0),
  (0.9968124473191577, 187.0),
  (0.9966647201929468, 2094.0),
  (0.9963368719335105, 2034.0),
  (0.996253663308771, 1556.0)],
 4031.0: [(0.9997694508982866, 114.0),
  (0.9995159502073636, 871.0),
  (0.9995080788349656, 252.0),
  (0.9994759450822637, 483.0),
  (0.9992938878790868, 744.0)],
 4193.0: [(0.9998012330137784, 483.0),
  (0.9994166523712883, 744.0),
  (0.9993869486907597, 871.0),
  (0.9988730572859409, 114.0),
  (0.9986942917044426, 2449.0)],
 4228.0: [(0.9998016756973064, 661.0),
  (0.9997450522613092, 114.0),
  (0.999699099673215, 252.0),
  (0.9994757316229307, 739.0),
  (0.9994365177808052, 2449.0)],
 4296.0: [(0.9996388454619307, 187.0),
  (0.9995347589135614, 1556.0),
  (0.9995334548204055, 2094.0),
  (0.9995247185596383, 2034.0),
  (0.9994704635604829, 2818.0)],
 4352.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4421.0),
  (0.9999999999999998, 3039.0),
  (0.9999999999999997, 2233.0)],
 4356.0: [(0.9997836684552204, 1086.0),
  (0.9996792394602323, 2072.0),
  (0.9995959451522555, 661.0),
  (0.9995079716949675, 1556.0),
  (0.9994955213481936, 1185.0)],
 4373.0: [(0.99974371730811, 114.0),
  (0.9997306167711404, 252.0),
  (0.9997090816643265, 661.0),
  (0.9995431886068592, 739.0),
  (0.999425180767316, 3918.0)],
 4421.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4352.0),
  (1.0, 3039.0),
  (1.0, 2233.0)],
 4447.0: [(1.0, 4671.0),
  (1.0, 4421.0),
  (1.0, 4352.0),
  (0.9999999999999998, 3039.0),
  (0.9999999999999998, 2638.0)],
 4514.0: [(0.9998577110523534, 3577.0),
  (0.9998461419687207, 114.0),
  (0.9998428737105022, 1556.0),
  (0.9998157838802103, 3036.0),
  (0.9998142918729963, 252.0)],
 4671.0: [(1.0, 4447.0),
  (1.0, 4421.0),
  (1.0, 4352.0),
  (1.0, 3039.0),
  (1.0, 2638.0)],
 4732.0: [(0.9997650278542047, 252.0),
  (0.9997157676950734, 114.0),
  (0.9995900548104198, 661.0),
  (0.9995508817312008, 871.0),
  (0.9994071554856044, 739.0)],
 4767.0: [(0.9995730280112732, 483.0),
  (0.9990710514446264, 871.0),
  (0.99884023331533, 114.0),
  (0.9985126614669165, 252.0),
  (0.9984756588774993, 744.0)],
 4874.0: [(0.9995033486387069, 44.0),
  (0.9971130729601648, 77.0),
  (0.9965959709105229, 35.0),
  (0.9959932933600324, 484.0),
  (0.99190025743775, 744.0)],
 4998.0: [(0.9995499628834562, 483.0),
  (0.9995248048484714, 744.0),
  (0.9988959369708852, 4228.0),
  (0.9988823289034996, 3367.0),
  (0.9987840961942054, 370.0)],
 5129.0: [(0.9995744137074402, 252.0),
  (0.9995056111179731, 187.0),
  (0.999495856607064, 94.0),
  (0.9994833681896999, 661.0),
  (0.9994130143379005, 739.0)],
 5131.0: [(0.9995383995781064, 871.0),
  (0.9993483939970514, 252.0),
  (0.9992364994548006, 744.0),
  (0.9991736642438355, 483.0),
  (0.9989658740401098, 114.0)],
 5193.0: [(0.9996879891302574, 2378.0),
  (0.9995397191396137, 1415.0),
  (0.9994246138142233, 2470.0),
  (0.9993859074547133, 3036.0),
  (0.9993777779672437, 1589.0)],
 5275.0: [(0.9994683203739051, 871.0),
  (0.9994195529759702, 1619.0),
  (0.9993205167430063, 2335.0),
  (0.9993056681112437, 483.0),
  (0.9991200845429938, 1556.0)],
 5317.0: [(0.9987518863033781, 2233.0),
  (0.9987518863033773, 2638.0),
  (0.9978532997363027, 1415.0),
  (0.9976941860538683, 4671.0),
  (0.9976941860538683, 4421.0)],
 5395.0: [(0.9998101613202225, 252.0),
  (0.999749533190111, 661.0),
  (0.9996852468959139, 2094.0),
  (0.9996367372707511, 3577.0),
  (0.9996314137270568, 4514.0)],
 5403.0: [(0.999527594375106, 484.0),
  (0.9994600912639361, 187.0),
  (0.9994148073518779, 2034.0),
  (0.9993474258144547, 1589.0),
  (0.9993110771280073, 2094.0)],
 5439.0: [(0.9997818522122607, 483.0),
  (0.9997654084332606, 3527.0),
  (0.9997597436085471, 4514.0),
  (0.999715473845256, 3577.0),
  (0.999658532154236, 3918.0)],
 5484.0: [(0.9997236408067696, 484.0),
  (0.9991288178853169, 187.0),
  (0.9990350699010342, 94.0),
  (0.9989495679513368, 2449.0),
  (0.9987288259494236, 1697.0)],
 5545.0: [(0.999483603343287, 114.0),
  (0.9993821350162491, 1086.0),
  (0.9993312772752735, 483.0),
  (0.9992130757430111, 871.0),
  (0.9990630054901386, 252.0)],
 5636.0: [(0.9996924511102975, 114.0),
  (0.999529949545906, 252.0),
  (0.9994547790845534, 744.0),
  (0.999436798396819, 661.0),
  (0.99940611594029, 187.0)],
 5658.0: [(0.9993757228336317, 1415.0),
  (0.9993143915428032, 94.0),
  (0.9992461919713654, 2818.0),
  (0.9991624375839349, 2378.0),
  (0.9991519908502043, 3723.0)],
 5785.0: [(0.9993759602206583, 2449.0),
  (0.999232776116856, 1507.0),
  (0.9992222635526711, 1556.0),
  (0.9991926082071966, 2094.0),
  (0.9991289925490332, 3310.0)],
 5810.0: [(0.9995423898608131, 35.0),
  (0.9995383440424508, 484.0),
  (0.9992806987974066, 661.0),
  (0.9992354122397664, 739.0),
  (0.9991662297543156, 1801.0)],
 5814.0: [(0.9998932895610143, 661.0),
  (0.9997871841251176, 252.0),
  (0.9997307954900639, 739.0),
  (0.9995547914702049, 1086.0),
  (0.999505549138197, 1589.0)],
 5892.0: [(0.9996189859987296, 94.0),
  (0.9995860634037128, 187.0),
  (0.9995326194805734, 3723.0),
  (0.9995287616478506, 2449.0),
  (0.9994785155428048, 739.0)],
 5972.0: [(0.9998185208042947, 1086.0),
  (0.9995893207938692, 661.0),
  (0.999385107346604, 1185.0),
  (0.999332659773839, 252.0),
  (0.9992259690328161, 2449.0)],
 6101.0: [(0.9989272819277072, 2814.0),
  (0.9981827631477523, 4447.0),
  (0.9981827631477127, 4421.0),
  (0.9981827631477127, 2233.0),
  (0.9981827631477126, 4352.0)],
 6412.0: [(0.9995990972299817, 744.0),
  (0.9995660447809152, 483.0),
  (0.9993050092573758, 3778.0),
  (0.9992517123182111, 114.0),
  (0.9990880836737225, 3310.0)],
 6505.0: [(0.9995100556247385, 744.0),
  (0.9994444352688393, 370.0),
  (0.9991304815034682, 2378.0),
  (0.9990578937302619, 2638.0),
  (0.9990578937302619, 2233.0)],
 6578.0: [(1.0, 4447.0),
  (1.0, 4421.0),
  (1.0, 4352.0),
  (1.0, 3039.0),
  (1.0, 2946.0)],
 6673.0: [(0.999409721302958, 94.0),
  (0.9993344282386112, 1791.0),
  (0.9991195486895835, 4296.0),
  (0.998964738940446, 1714.0),
  (0.9989072974592853, 739.0)],
 6685.0: [(0.9991712474470051, 35.0),
  (0.9991366350645564, 44.0),
  (0.9988036429430099, 484.0),
  (0.9977100202227116, 2645.0),
  (0.9977096333758422, 187.0)],
 6830.0: [(0.9997944124653904, 1556.0),
  (0.9997831892464042, 2094.0),
  (0.9996269132240623, 4514.0),
  (0.9996225472339201, 2449.0),
  (0.9996125820274235, 3577.0)],
 6836.0: [(0.9996223829801588, 2378.0),
  (0.99960945115434, 1589.0),
  (0.9995643711124965, 2818.0),
  (0.9995395678534865, 4296.0),
  (0.9995222351874783, 2470.0)],
 6863.0: [(0.9996028242950981, 44.0),
  (0.9995527395465583, 35.0),
  (0.9986431951834557, 484.0),
  (0.9984363928931744, 1801.0),
  (0.9983688906032885, 2378.0)],
 6910.0: [(0.9997247585445533, 252.0),
  (0.9996474288338911, 114.0),
  (0.9996227846757719, 661.0),
  (0.9995989330904198, 2946.0),
  (0.999576833080367, 4373.0)],
 7016.0: [(0.9984393364141333, 483.0),
  (0.9979493589161295, 1103.0),
  (0.9976432290344541, 114.0),
  (0.9973944911252135, 370.0),
  (0.9973917592796485, 744.0)],
 7017.0: [(0.9996888012864305, 483.0),
  (0.9996200029130459, 2094.0),
  (0.9995312134848215, 1556.0),
  (0.9993852511156494, 2034.0),
  (0.9993270110465315, 2449.0)],
 7030.0: [(0.9994026322061771, 1042.0),
  (0.9990143869633445, 187.0),
  (0.9981578404094669, 484.0),
  (0.9978875488863308, 114.0),
  (0.9975032686142777, 94.0)],
 7117.0: [(0.999803052914747, 370.0),
  (0.9993671845113258, 4296.0),
  (0.9992720423723768, 2018.0),
  (0.9992071537132763, 2818.0),
  (0.999196644819564, 2461.0)],
 7287.0: [(0.9994900695258923, 1086.0),
  (0.9993992813044519, 739.0),
  (0.9993924548429731, 661.0),
  (0.9993537243277547, 2449.0),
  (0.9992899116100401, 1556.0)],
 7429.0: [(0.9995505986854867, 744.0),
  (0.9993227429538454, 114.0),
  (0.9992750556902509, 483.0),
  (0.9991326978651441, 871.0),
  (0.9989414451894282, 252.0)],
 7460.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4352.0),
  (1.0, 3039.0),
  (1.0, 2638.0)],
 7566.0: [(0.9996792843637766, 44.0),
  (0.9993757293562842, 4447.0),
  (0.9993757293562542, 3039.0),
  (0.9993757293562537, 4421.0),
  (0.9993757293562535, 4671.0)],
 7674.0: [(0.9993721136655437, 114.0),
  (0.9993523170435376, 744.0),
  (0.9992502242490081, 483.0),
  (0.9990448858360059, 187.0),
  (0.9988797470035449, 2461.0)],
 7682.0: [(0.999182624218682, 1086.0),
  (0.9989396394358206, 35.0),
  (0.9989286584813644, 1185.0),
  (0.9988885417448476, 2575.0),
  (0.9987762487114724, 2378.0)],
 7739.0: [(0.9993231945508513, 1507.0),
  (0.9991090361509631, 2378.0),
  (0.9989147332984178, 2470.0),
  (0.9988905351808346, 2034.0),
  (0.998820462440802, 2094.0)],
 7741.0: [(0.9996807164625034, 187.0),
  (0.9995615919004249, 2378.0),
  (0.9995531699807663, 1507.0),
  (0.9995351638191452, 2470.0),
  (0.9995178738840471, 1589.0)],
 7794.0: [(0.9999050097436575, 661.0),
  (0.9997813198550072, 252.0),
  (0.9997069295434086, 114.0),
  (0.9996993078639038, 739.0),
  (0.9995212362618088, 1556.0)],
 7900.0: [(0.9998289666278423, 744.0),
  (0.9996567705391409, 483.0),
  (0.9995859983505495, 1507.0),
  (0.9995523833067727, 2378.0),
  (0.9994694081151799, 2034.0)],
 7919.0: [(1.0, 4447.0),
  (1.0, 4421.0),
  (1.0, 4352.0),
  (1.0, 3039.0),
  (0.9999999999999999, 4671.0)],
 7965.0: [(0.9999768825803856, 2638.0),
  (0.9999768825803853, 2233.0),
  (0.9999369695062252, 4447.0),
  (0.9999369695062087, 4352.0),
  (0.9999369695062084, 3039.0)],
 7989.0: [(0.999601740977677, 187.0),
  (0.9994508912040354, 484.0),
  (0.9993693028316634, 1507.0),
  (0.9993505999285083, 35.0),
  (0.9993235047477294, 744.0)],
 8059.0: [(0.9995315111999052, 114.0),
  (0.9989299553847921, 252.0),
  (0.998888132307771, 661.0),
  (0.9983248878869074, 187.0),
  (0.998294442546095, 739.0)],
 8084.0: [(0.9990615615232697, 3310.0),
  (0.9989730562825202, 4296.0),
  (0.9988047663448092, 4514.0),
  (0.9987232488039237, 3577.0),
  (0.9986686089325842, 3134.0)],
 8086.0: [(0.9995736031220369, 187.0),
  (0.9994957428169806, 2449.0),
  (0.9993156700098379, 744.0),
  (0.9992618973245356, 2094.0),
  (0.9992486852087383, 3310.0)],
 8155.0: [(0.9971876249754231, 1086.0),
  (0.9971814199623387, 1185.0),
  (0.9955764221692988, 661.0),
  (0.9954669724780262, 739.0),
  (0.9950613188926989, 252.0)],
 8156.0: [(0.999260171016417, 2449.0),
  (0.9990923154920868, 187.0),
  (0.9989459143546673, 114.0),
  (0.9988441154706881, 2461.0),
  (0.9988407027126288, 252.0)],
 8244.0: [(0.9990715639531355, 1697.0),
  (0.9990066190738762, 2449.0),
  (0.9989703872350274, 187.0),
  (0.9989362822185957, 3723.0),
  (0.9988854495084363, 1042.0)],
 8386.0: [(0.999607896454054, 1556.0),
  (0.9995920784885469, 2094.0),
  (0.9995786348145564, 2449.0),
  (0.9995698086547984, 1507.0),
  (0.9995168817631669, 2755.0)],
 8467.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4421.0),
  (1.0, 4352.0),
  (0.9999999999999998, 2638.0)],
 8703.0: [(0.9994480262012427, 2946.0),
  (0.9991974001890621, 3527.0),
  (0.9991589601354945, 1507.0),
  (0.9991492566759569, 1042.0),
  (0.9990189321821605, 3036.0)],
 8829.0: [(0.9997245953934595, 744.0),
  (0.9996672816181719, 483.0),
  (0.9995488150368462, 1556.0),
  (0.9995083655147161, 2094.0),
  (0.9995061962295115, 2072.0)],
 8890.0: [(0.9997617233337401, 1556.0),
  (0.999735990705161, 252.0),
  (0.9996816993436427, 871.0),
  (0.9996752872354413, 2094.0),
  (0.9996355806530609, 2378.0)],
 8967.0: [(0.9349620648937742, 1185.0),
  (0.9301382898231378, 1086.0),
  (0.9262336715350215, 77.0),
  (0.9218577739284818, 661.0),
  (0.9212516059473377, 739.0)],
 9052.0: [(0.9997323884649234, 252.0),
  (0.9997198021148013, 114.0),
  (0.9996825142915314, 1556.0),
  (0.999610731549192, 871.0),
  (0.9995717702045968, 2094.0)],
 9121.0: [(0.9998503064727738, 661.0),
  (0.9996789947247197, 1086.0),
  (0.9996053713440647, 739.0),
  (0.9994796218825788, 252.0),
  (0.9993397375542541, 114.0)],
 9134.0: [(0.9998772444997509, 661.0),
  (0.9998063313597616, 2378.0),
  (0.999734026973719, 739.0),
  (0.9997261755610352, 252.0),
  (0.9995692807853458, 1589.0)],
 9160.0: [(0.9992148831998982, 187.0),
  (0.9990973888058959, 1042.0),
  (0.998869287562428, 2449.0),
  (0.9988511911264534, 661.0),
  (0.9987305585297322, 1283.0)],
 9278.0: [(0.9948654683481162, 1086.0),
  (0.9939634060574246, 1185.0),
  (0.9929827949813879, 739.0),
  (0.9926036748586705, 661.0),
  (0.9919041240113539, 1403.0)],
 9295.0: [(0.9998048492159837, 739.0),
  (0.9996822238818616, 2378.0),
  (0.9996710095899275, 661.0),
  (0.9996708363327949, 1507.0),
  (0.9996356491991651, 1086.0)],
 9474.0: [(1.0, 4671.0),
  (1.0, 4421.0),
  (1.0, 3039.0),
  (1.0, 2638.0),
  (1.0, 2233.0)],
 9600.0: [(0.9994149016856002, 187.0),
  (0.9993899320299854, 661.0),
  (0.9993878296075039, 744.0),
  (0.9993371747385331, 1415.0),
  (0.999285760022726, 2378.0)],
 9620.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4421.0),
  (1.0, 4352.0),
  (0.9999999999999994, 3039.0)],
 9631.0: [(0.9994140952479954, 3544.0),
  (0.9993739399377937, 2449.0),
  (0.9993093163660373, 4514.0),
  (0.9992928332669202, 2094.0),
  (0.9992771432471899, 1556.0)],
 9639.0: [(0.9995367522839229, 1086.0),
  (0.9993518433321407, 1415.0),
  (0.999333365938334, 1556.0),
  (0.9993269061027523, 2470.0),
  (0.9993151681730542, 2378.0)],
 9729.0: [(0.9995615158937515, 114.0),
  (0.9995036202859752, 2072.0),
  (0.9994971525975482, 483.0),
  (0.9992984948959214, 252.0),
  (0.9992848309184342, 2094.0)],
 9766.0: [(0.9997250210182815, 187.0),
  (0.9993786178475147, 114.0),
  (0.9991511673123585, 252.0),
  (0.9991021756649142, 94.0),
  (0.9990853557136227, 484.0)],
 9849.0: [(0.9994786802345867, 77.0),
  (0.9978096991639781, 44.0),
  (0.9976300740170693, 35.0),
  (0.9974517252563948, 2645.0),
  (0.9974024522280461, 1801.0)],
 9956.0: [(1.0, 4671.0),
  (1.0, 4447.0),
  (1.0, 4352.0),
  (0.9999999999999998, 3039.0),
  (0.9999999999999996, 4421.0)],
 9982.0: [(0.9995129966422505, 2094.0),
  (0.9994414280579035, 1415.0),
  (0.9994355238722538, 1589.0),
  (0.9993973596854848, 2470.0),
  (0.9993853174605507, 1556.0)]}


# Convert dict into df corr matrix
corr_df = pd.DataFrame()
for key, val in results.items():
    for res in val:
        corr_df.at[key, res[1]] = res[0]


corr_df


import seaborn as sns
import matplotlib.pyplot as plt

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(30, 30))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# cmap="YlGnBu" #alternative color map
sns.heatmap(corr_df, cmap=cmap, vmax=.3, center=0, xticklabels=True,
            square=True, linewidths=.5, cbar_kws={"shrink": .7})

###### plot using matlab (even uglier though)
# f = plt.figure(figsize=(19, 15))
# plt.matshow(corr_df, fignum=f.number)
# plt.xticks(range(corr_df.shape[1]), corr_df.columns, fontsize=14, rotation=45)
# plt.yticks(range(corr_df.shape[1]), corr_df.columns, fontsize=14)
# cb = plt.colorbar()
# cb.ax.tick_params(labelsize=14)
# plt.title('Correlation Matrix', fontsize=16);

<AxesSubplot:>


### Use builtin heatmap plotter from pandas
corr_df.style.background_gradient(cmap='coolwarm')


### Plot network graph source:https://python-graph-gallery.com/327-network-from-correlation-matrix/
import networkx as nx
# Transform it in a links data frame (3 columns only):
links = corr_df.stack().reset_index()
links.columns = ['var1', 'var2','value']
links
 
# Keep only correlation over a threshold and remove self correlation (cor(A,A)=1)
links_filtered=links.loc[ (links['value'] > 0.8) & (links['var1'] != links['var2']) ]
links_filtered

# Build your graph
G=nx.from_pandas_edgelist(links_filtered, 'var1', 'var2')
# Plot the network:
nx.draw(G, with_labels=True,
        node_color='cyan',
        node_size=100,
        edge_color='black',
        linewidths=0.05, font_size=5)


#with align
for item in results.items(): 
    print(item)

(35.0, [(0.9988916513276471, 1801.0), (0.9987725928850879, 484.0), (0.9987715294996004, 44.0), (0.998713916885043, 2378.0), (0.9982606893127842, 1086.0)])
(44.0, [(0.9989856933664459, 1697.0), (0.9989041421451049, 2378.0), (0.9988639130515852, 2449.0), (0.9987868775083375, 1507.0), (0.9987715294996004, 35.0)])
(77.0, [(0.9985464337783981, 2575.0), (0.9985076194482484, 2378.0), (0.9984191125503111, 1415.0), (0.9984157534979128, 1697.0), (0.9981987354565149, 1556.0)])
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(5658.0, [(0.9993757228336317, 1415.0), (0.9993143915428032, 94.0), (0.9992461919713654, 2818.0), (0.9991624375839349, 2378.0), (0.9991519908502043, 3723.0)])
(5785.0, [(0.9993759602206583, 2449.0), (0.999232776116856, 1507.0), (0.9992222635526711, 1556.0), (0.9991926082071966, 2094.0), (0.9991289925490332, 3310.0)])
(5810.0, [(0.9995423898608131, 35.0), (0.9995383440424508, 484.0), (0.9992806987974066, 661.0), (0.9992354122397664, 739.0), (0.9991662297543156, 1801.0)])
(5814.0, [(0.9998932895610143, 661.0), (0.9997871841251176, 252.0), (0.9997307954900639, 739.0), (0.9995547914702049, 1086.0), (0.999505549138197, 1589.0)])
(5892.0, [(0.9996189859987296, 94.0), (0.9995860634037128, 187.0), (0.9995326194805734, 3723.0), (0.9995287616478506, 2449.0), (0.9994785155428048, 739.0)])
(5972.0, [(0.9998185208042947, 1086.0), (0.9995893207938692, 661.0), (0.999385107346604, 1185.0), (0.999332659773839, 252.0), (0.9992259690328161, 2449.0)])
(6101.0, [(0.9989272819277072, 2814.0), (0.9981827631477523, 4447.0), (0.9981827631477127, 4421.0), (0.9981827631477127, 2233.0), (0.9981827631477126, 4352.0)])
(6412.0, [(0.9995990972299817, 744.0), (0.9995660447809152, 483.0), (0.9993050092573758, 3778.0), (0.9992517123182111, 114.0), (0.9990880836737225, 3310.0)])
(6505.0, [(0.9995100556247385, 744.0), (0.9994444352688393, 370.0), (0.9991304815034682, 2378.0), (0.9990578937302619, 2638.0), (0.9990578937302619, 2233.0)])
(6578.0, [(1.0, 4447.0), (1.0, 4421.0), (1.0, 4352.0), (1.0, 3039.0), (1.0, 2946.0)])
(6673.0, [(0.999409721302958, 94.0), (0.9993344282386112, 1791.0), (0.9991195486895835, 4296.0), (0.998964738940446, 1714.0), (0.9989072974592853, 739.0)])
(6685.0, [(0.9991712474470051, 35.0), (0.9991366350645564, 44.0), (0.9988036429430099, 484.0), (0.9977100202227116, 2645.0), (0.9977096333758422, 187.0)])
(6830.0, [(0.9997944124653904, 1556.0), (0.9997831892464042, 2094.0), (0.9996269132240623, 4514.0), (0.9996225472339201, 2449.0), (0.9996125820274235, 3577.0)])
(6836.0, [(0.9996223829801588, 2378.0), (0.99960945115434, 1589.0), (0.9995643711124965, 2818.0), (0.9995395678534865, 4296.0), (0.9995222351874783, 2470.0)])
(6863.0, [(0.9996028242950981, 44.0), (0.9995527395465583, 35.0), (0.9986431951834557, 484.0), (0.9984363928931744, 1801.0), (0.9983688906032885, 2378.0)])
(6910.0, [(0.9997247585445533, 252.0), (0.9996474288338911, 114.0), (0.9996227846757719, 661.0), (0.9995989330904198, 2946.0), (0.999576833080367, 4373.0)])
(7016.0, [(0.9984393364141333, 483.0), (0.9979493589161295, 1103.0), (0.9976432290344541, 114.0), (0.9973944911252135, 370.0), (0.9973917592796485, 744.0)])
(7017.0, [(0.9996888012864305, 483.0), (0.9996200029130459, 2094.0), (0.9995312134848215, 1556.0), (0.9993852511156494, 2034.0), (0.9993270110465315, 2449.0)])
(7030.0, [(0.9994026322061771, 1042.0), (0.9990143869633445, 187.0), (0.9981578404094669, 484.0), (0.9978875488863308, 114.0), (0.9975032686142777, 94.0)])
(7117.0, [(0.999803052914747, 370.0), (0.9993671845113258, 4296.0), (0.9992720423723768, 2018.0), (0.9992071537132763, 2818.0), (0.999196644819564, 2461.0)])
(7287.0, [(0.9994900695258923, 1086.0), (0.9993992813044519, 739.0), (0.9993924548429731, 661.0), (0.9993537243277547, 2449.0), (0.9992899116100401, 1556.0)])
(7429.0, [(0.9995505986854867, 744.0), (0.9993227429538454, 114.0), (0.9992750556902509, 483.0), (0.9991326978651441, 871.0), (0.9989414451894282, 252.0)])
(7460.0, [(1.0, 4671.0), (1.0, 4447.0), (1.0, 4352.0), (1.0, 3039.0), (1.0, 2638.0)])
(7566.0, [(0.9996792843637766, 44.0), (0.9993757293562842, 4447.0), (0.9993757293562542, 3039.0), (0.9993757293562537, 4421.0), (0.9993757293562535, 4671.0)])
(7674.0, [(0.9993721136655437, 114.0), (0.9993523170435376, 744.0), (0.9992502242490081, 483.0), (0.9990448858360059, 187.0), (0.9988797470035449, 2461.0)])
(7682.0, [(0.999182624218682, 1086.0), (0.9989396394358206, 35.0), (0.9989286584813644, 1185.0), (0.9988885417448476, 2575.0), (0.9987762487114724, 2378.0)])
(7739.0, [(0.9993231945508513, 1507.0), (0.9991090361509631, 2378.0), (0.9989147332984178, 2470.0), (0.9988905351808346, 2034.0), (0.998820462440802, 2094.0)])
(7741.0, [(0.9996807164625034, 187.0), (0.9995615919004249, 2378.0), (0.9995531699807663, 1507.0), (0.9995351638191452, 2470.0), (0.9995178738840471, 1589.0)])
(7794.0, [(0.9999050097436575, 661.0), (0.9997813198550072, 252.0), (0.9997069295434086, 114.0), (0.9996993078639038, 739.0), (0.9995212362618088, 1556.0)])
(7900.0, [(0.9998289666278423, 744.0), (0.9996567705391409, 483.0), (0.9995859983505495, 1507.0), (0.9995523833067727, 2378.0), (0.9994694081151799, 2034.0)])
(7919.0, [(1.0, 4447.0), (1.0, 4421.0), (1.0, 4352.0), (1.0, 3039.0), (0.9999999999999999, 4671.0)])
(7965.0, [(0.9999768825803856, 2638.0), (0.9999768825803853, 2233.0), (0.9999369695062252, 4447.0), (0.9999369695062087, 4352.0), (0.9999369695062084, 3039.0)])
(7989.0, [(0.999601740977677, 187.0), (0.9994508912040354, 484.0), (0.9993693028316634, 1507.0), (0.9993505999285083, 35.0), (0.9993235047477294, 744.0)])
(8059.0, [(0.9995315111999052, 114.0), (0.9989299553847921, 252.0), (0.998888132307771, 661.0), (0.9983248878869074, 187.0), (0.998294442546095, 739.0)])
(8084.0, [(0.9990615615232697, 3310.0), (0.9989730562825202, 4296.0), (0.9988047663448092, 4514.0), (0.9987232488039237, 3577.0), (0.9986686089325842, 3134.0)])
(8086.0, [(0.9995736031220369, 187.0), (0.9994957428169806, 2449.0), (0.9993156700098379, 744.0), (0.9992618973245356, 2094.0), (0.9992486852087383, 3310.0)])
(8155.0, [(0.9971876249754231, 1086.0), (0.9971814199623387, 1185.0), (0.9955764221692988, 661.0), (0.9954669724780262, 739.0), (0.9950613188926989, 252.0)])
(8156.0, [(0.999260171016417, 2449.0), (0.9990923154920868, 187.0), (0.9989459143546673, 114.0), (0.9988441154706881, 2461.0), (0.9988407027126288, 252.0)])
(8244.0, [(0.9990715639531355, 1697.0), (0.9990066190738762, 2449.0), (0.9989703872350274, 187.0), (0.9989362822185957, 3723.0), (0.9988854495084363, 1042.0)])
(8386.0, [(0.999607896454054, 1556.0), (0.9995920784885469, 2094.0), (0.9995786348145564, 2449.0), (0.9995698086547984, 1507.0), (0.9995168817631669, 2755.0)])
(8467.0, [(1.0, 4671.0), (1.0, 4447.0), (1.0, 4421.0), (1.0, 4352.0), (0.9999999999999998, 2638.0)])
(8703.0, [(0.9994480262012427, 2946.0), (0.9991974001890621, 3527.0), (0.9991589601354945, 1507.0), (0.9991492566759569, 1042.0), (0.9990189321821605, 3036.0)])
(8829.0, [(0.9997245953934595, 744.0), (0.9996672816181719, 483.0), (0.9995488150368462, 1556.0), (0.9995083655147161, 2094.0), (0.9995061962295115, 2072.0)])
(8890.0, [(0.9997617233337401, 1556.0), (0.999735990705161, 252.0), (0.9996816993436427, 871.0), (0.9996752872354413, 2094.0), (0.9996355806530609, 2378.0)])
(8967.0, [(0.9349620648937742, 1185.0), (0.9301382898231378, 1086.0), (0.9262336715350215, 77.0), (0.9218577739284818, 661.0), (0.9212516059473377, 739.0)])
(9052.0, [(0.9997323884649234, 252.0), (0.9997198021148013, 114.0), (0.9996825142915314, 1556.0), (0.999610731549192, 871.0), (0.9995717702045968, 2094.0)])
(9121.0, [(0.9998503064727738, 661.0), (0.9996789947247197, 1086.0), (0.9996053713440647, 739.0), (0.9994796218825788, 252.0), (0.9993397375542541, 114.0)])
(9134.0, [(0.9998772444997509, 661.0), (0.9998063313597616, 2378.0), (0.999734026973719, 739.0), (0.9997261755610352, 252.0), (0.9995692807853458, 1589.0)])
(9160.0, [(0.9992148831998982, 187.0), (0.9990973888058959, 1042.0), (0.998869287562428, 2449.0), (0.9988511911264534, 661.0), (0.9987305585297322, 1283.0)])
(9278.0, [(0.9948654683481162, 1086.0), (0.9939634060574246, 1185.0), (0.9929827949813879, 739.0), (0.9926036748586705, 661.0), (0.9919041240113539, 1403.0)])
(9295.0, [(0.9998048492159837, 739.0), (0.9996822238818616, 2378.0), (0.9996710095899275, 661.0), (0.9996708363327949, 1507.0), (0.9996356491991651, 1086.0)])
(9474.0, [(1.0, 4671.0), (1.0, 4421.0), (1.0, 3039.0), (1.0, 2638.0), (1.0, 2233.0)])
(9600.0, [(0.9994149016856002, 187.0), (0.9993899320299854, 661.0), (0.9993878296075039, 744.0), (0.9993371747385331, 1415.0), (0.999285760022726, 2378.0)])
(9620.0, [(1.0, 4671.0), (1.0, 4447.0), (1.0, 4421.0), (1.0, 4352.0), (0.9999999999999994, 3039.0)])
(9631.0, [(0.9994140952479954, 3544.0), (0.9993739399377937, 2449.0), (0.9993093163660373, 4514.0), (0.9992928332669202, 2094.0), (0.9992771432471899, 1556.0)])
(9639.0, [(0.9995367522839229, 1086.0), (0.9993518433321407, 1415.0), (0.999333365938334, 1556.0), (0.9993269061027523, 2470.0), (0.9993151681730542, 2378.0)])
(9729.0, [(0.9995615158937515, 114.0), (0.9995036202859752, 2072.0), (0.9994971525975482, 483.0), (0.9992984948959214, 252.0), (0.9992848309184342, 2094.0)])
(9766.0, [(0.9997250210182815, 187.0), (0.9993786178475147, 114.0), (0.9991511673123585, 252.0), (0.9991021756649142, 94.0), (0.9990853557136227, 484.0)])
(9849.0, [(0.9994786802345867, 77.0), (0.9978096991639781, 44.0), (0.9976300740170693, 35.0), (0.9974517252563948, 2645.0), (0.9974024522280461, 1801.0)])
(9956.0, [(1.0, 4671.0), (1.0, 4447.0), (1.0, 4352.0), (0.9999999999999998, 3039.0), (0.9999999999999996, 4421.0)])
(9982.0, [(0.9995129966422505, 2094.0), (0.9994414280579035, 1415.0), (0.9994355238722538, 1589.0), (0.9993973596854848, 2470.0), (0.9993853174605507, 1556.0)])

	1801.0	484.0	44.0	2378.0	1086.0	1697.0	2449.0	1507.0	35.0	2575.0	...	4296.0	1185.0	3367.0	1619.0	3310.0	2814.0	2946.0	3134.0	1283.0	1403.0
35.0	0.998892	0.998773	0.998772	0.998714	0.998261	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
44.0	NaN	NaN	NaN	0.998904	NaN	0.998986	0.998864	0.998787	0.998772	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
77.0	NaN	NaN	NaN	0.998508	NaN	0.998416	NaN	NaN	NaN	0.998546	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
94.0	NaN	NaN	NaN	0.999800	NaN	NaN	NaN	0.999532	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
114.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9729.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9766.0	NaN	0.999085	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9849.0	0.997402	NaN	0.997810	NaN	NaN	NaN	NaN	NaN	0.997630	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9956.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9982.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Question 1¶

Q1.1¶

Q1.2¶

Q1.3¶

Q1.3 Corr matrix without spikes, network graph, heatmap¶

	1801.0	484.0	44.0	2378.0	1086.0	1697.0	2449.0	1507.0	35.0	2575.0	1415.0	1556.0	2818.0	739.0	252.0	2094.0	2034.0	661.0	2755.0	3723.0	1792.0	77.0	4373.0	2461.0	2018.0	114.0	871.0	744.0	483.0	1103.0	187.0	3527.0	3544.0	94.0	2645.0	2470.0	1589.0	3918.0	1791.0	4514.0	2072.0	3577.0	1714.0	2335.0	3036.0	370.0	1042.0	1790.0	3778.0	4671.0	4447.0	4421.0	4352.0	3039.0	4228.0	2233.0	2638.0	4356.0	4296.0	1185.0	3367.0	1619.0	3310.0	2814.0	2946.0	3134.0	1283.0	1403.0
35.0	0.998892	0.998773	0.998772	0.998714	0.998261	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
44.0	nan	nan	nan	0.998904	nan	0.998986	0.998864	0.998787	0.998772	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
77.0	nan	nan	nan	0.998508	nan	0.998416	nan	nan	nan	0.998546	0.998419	0.998199	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
94.0	nan	nan	nan	0.999800	nan	nan	nan	0.999532	nan	nan	nan	0.999398	0.999521	0.999410	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
114.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999563	nan	nan	0.999526	0.999510	0.999505	0.999478	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
187.0	nan	nan	nan	nan	nan	nan	0.999624	nan	nan	nan	nan	0.999643	nan	nan	nan	0.999625	nan	nan	0.999459	0.999363	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
222.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.993326	0.987456	0.986769	0.986732	0.986284	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
252.0	nan	nan	nan	nan	0.999197	nan	nan	nan	nan	nan	nan	nan	nan	0.999435	nan	nan	nan	0.999728	nan	nan	nan	nan	nan	nan	nan	0.999526	0.999516	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
370.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.996876	0.999128	0.998664	0.998088	0.997309	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
483.0	nan	nan	nan	nan	nan	nan	0.999219	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999293	0.999238	nan	nan	nan	0.999155	0.999105	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
484.0	nan	nan	nan	nan	nan	nan	nan	nan	0.998518	nan	nan	nan	nan	0.998539	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999259	nan	nan	0.999146	0.998903	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
661.0	nan	nan	nan	nan	0.999652	nan	nan	nan	nan	nan	nan	0.999550	nan	0.999820	0.999728	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999618	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
739.0	nan	nan	nan	nan	0.999526	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999435	nan	nan	0.999820	nan	nan	nan	nan	nan	nan	nan	0.999194	nan	nan	nan	nan	nan	nan	nan	0.999410	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
744.0	nan	nan	nan	0.999412	nan	nan	nan	0.999500	nan	nan	0.999421	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999574	0.999457	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
871.0	nan	nan	nan	nan	nan	nan	nan	0.999538	nan	nan	nan	0.999541	nan	nan	nan	0.999705	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999555	0.999530	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1042.0	nan	nan	nan	nan	nan	nan	0.999420	nan	nan	nan	nan	0.999177	nan	nan	nan	0.999159	nan	nan	nan	nan	nan	nan	nan	nan	0.999046	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999082	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1086.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999526	nan	nan	nan	0.999652	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999686	0.999597	nan	nan	0.999519	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1103.0	nan	nan	nan	0.999668	nan	nan	nan	0.999623	nan	nan	nan	0.999684	nan	nan	nan	0.999658	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999632	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1185.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999643	nan	nan	nan	0.999703	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999597	0.999677	0.999593	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1283.0	nan	nan	nan	0.999003	nan	nan	nan	nan	nan	nan	0.998954	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999009	nan	nan	nan	nan	nan	0.999218	0.999128	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1403.0	0.986859	0.989618	nan	nan	nan	nan	nan	nan	0.988116	nan	nan	nan	nan	0.989745	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.987915	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1415.0	nan	nan	nan	0.999593	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999674	nan	nan	nan	nan	nan	nan	nan	0.999586	0.999482	nan	nan	nan	nan	nan	nan	nan	0.999424	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1507.0	nan	nan	nan	0.999737	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999587	nan	nan	nan	nan	0.999599	nan	nan	nan	nan	0.999649	nan	nan	nan	0.999593	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1556.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999763	0.999769	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999893	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999678	nan	nan	nan	nan	0.999678	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1589.0	nan	nan	nan	0.999567	nan	nan	nan	0.999549	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999809	nan	nan	nan	nan	nan	0.999537	nan	nan	nan	0.999756	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1619.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999677	nan	nan	0.999352	nan	nan	nan	nan	nan	nan	nan	0.999830	0.999535	nan	nan	nan	nan	0.999360	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1697.0	nan	0.999312	0.998968	nan	nan	nan	0.998687	nan	0.999476	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.998520	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1714.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999006	0.999192	nan	nan	nan	nan	nan	nan	nan	0.999045	nan	0.998975	nan	nan	nan	nan	nan	nan	0.999414	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1718.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999254	nan	nan	0.999101	nan	nan	nan	nan	nan	nan	nan	0.999787	nan	0.998933	nan	nan	0.999162	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1790.0	nan	nan	nan	nan	0.999362	nan	nan	nan	nan	nan	nan	nan	nan	0.999299	0.999101	nan	nan	0.999379	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999008	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1791.0	nan	nan	nan	nan	nan	nan	0.999250	nan	nan	nan	nan	nan	nan	nan	nan	0.999153	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999621	nan	0.999133	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999322	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1792.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999057	nan	nan	0.998892	nan	nan	nan	nan	nan	nan	nan	0.999610	0.998753	nan	0.998608	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1800.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.997600	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.997105	0.997722	nan	nan	0.998146	0.997911	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
1801.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999206	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999659	nan	0.999309	nan	nan	0.999467	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999173	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2018.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.998858	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999036	nan	0.999067	0.998899	nan	0.999599	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2034.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999543	nan	nan	nan	0.999576	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999644	nan	0.999449	0.999569	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2072.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999526	nan	nan	nan	0.999513	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999594	0.999619	nan	0.999442	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2094.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999769	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999838	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999627	nan	0.999656	nan	nan	0.999587	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2129.0	nan	nan	nan	nan	0.999093	nan	nan	nan	nan	nan	nan	nan	nan	0.999336	0.999215	nan	nan	0.999498	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.999006	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2233.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	1.000000	1.000000	1.000000	1.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan